Score-Driven Exponential Random Graphs: A New Class of Time-Varying Parameter Models for Dynamical Networks
Score-Driven Exponential Random Graphs: A New Class of Time-Varying Parameter Models for Dynamical Networks
Motivated by the increasing abundance of data describing real-world networks that exhibit dynamical features, we propose an extension of the Exponential Random Graph Models (ERGMs) that accommodates the time variation of its parameters. Inspired by the fast-growing literature on Dynamic Conditional Score models, each parameter evolves according to an updating rule driven by the score of the ERGM distribution. We demonstrate the flexibility of score-driven ERGMs (SD-ERGMs) as data-generating processes and filters and show the advantages of the dynamic version over the static one. We discuss two applications to temporal networks from financial and political systems. First, we consider the prediction of future links in the Italian interbank credit network. Second, we show that the SD-ERGM allows discriminating between static or time-varying parameters when used to model the U.S. Congress co-voting network dynamics.
Domenico Di Gangi、Fabrizio Lillo、Giacomo Bormetti
财政、金融数学计算技术、计算机技术
Domenico Di Gangi,Fabrizio Lillo,Giacomo Bormetti.Score-Driven Exponential Random Graphs: A New Class of Time-Varying Parameter Models for Dynamical Networks[EB/OL].(2019-05-26)[2025-08-23].https://arxiv.org/abs/1905.10806.点此复制
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